{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from PIL import Image\n",
    "\n",
    "from lavis.models import load_model_and_preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load an example image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_image = Image.open(\"../docs/_static/merlion.png\").convert(\"RGB\")\n",
    "caption = \"a large fountain spewing water into the air\"\n",
    "\n",
    "display(raw_image.resize((596, 437)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup device to use\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, vis_processors, txt_processors = load_model_and_preprocess(name=\"blip2_feature_extractor\", model_type=\"pretrain\", is_eval=True, device=device)\n",
    "image = vis_processors[\"eval\"](raw_image).unsqueeze(0).to(device)\n",
    "text_input = txt_processors[\"eval\"](caption)\n",
    "sample = {\"image\": image, \"text_input\": [text_input]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multimodal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_multimodal = model.extract_features(sample)\n",
    "print(features_multimodal.multimodal_embeds.shape)\n",
    "# torch.Size([1, 32, 768]), 32 is the number of queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unimodal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_image = model.extract_features(sample, mode=\"image\")\n",
    "features_text = model.extract_features(sample, mode=\"text\")\n",
    "print(features_image.image_embeds.shape)\n",
    "# torch.Size([1, 32, 768])\n",
    "print(features_text.text_embeds.shape)\n",
    "# torch.Size([1, 12, 768])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalized low-dimensional unimodal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# low-dimensional projected features\n",
    "print(features_image.image_embeds_proj.shape)\n",
    "# torch.Size([1, 32, 256])\n",
    "print(features_text.text_embeds_proj.shape)\n",
    "# torch.Size([1, 12, 256])\n",
    "similarity = (features_image.image_embeds_proj @ features_text.text_embeds_proj[:,0,:].t()).max()\n",
    "print(similarity)\n",
    "# tensor([[0.3642]])"
   ]
  }
 ],
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